Course Coordinates

Taught at the University of Konstanz by Hansjörg Neth (h.neth@uni.kn, SPDS, office D507).
Winter 2018/2019: Mondays, 13:30–15:00, C511.
Links to current course syllabus | ZeUS | Ilias

Logo

Overview over generated plots:

Tiles

Showing the versions with borders:

Tile plots (random)

Tile plots (sorted)

Tile plots (with borders and numeric labels)

Random versions:

Sorted versions:

Poles

Showing the versions without borders:

Pole plots (random)

Pole plots (sorted)

Pole plots (with borders and numeric labels)

Random versions:

Sorted versions:

Settings

Text

# Define text labels:
lbl_psi <- expression(psi)
course_title <- paste0("Data science\nfor psychologists")
course_title_abb <- paste0("ds4psy")

Data

# Dimensions:
n_rand <- sample(1:15, size = 1, replace = TRUE)  # random integer in range

n_x <- n_rand  # random number
# n_x <- 9       # a specific number
n_y <- n_x
N <- (n_x * n_y)

# Vectors: 
v_sort <- 1:N       # Tile: bottom = seeblau; top = black | Polar: outer = black, center = seeblau.
v_sort <- rev(1:N)  # Tile: bottom = black; top = seeblau | Polar: outer = seeblau, center = black.

set.seed(123)  # for reproducible randomness
# v_rand <- runif(n = N, 0, 1)
v_rand <- sample(v_sort, N)  # random permutation of v_sort

# Table: 
tb <- tibble(y = rep(1:n_x, each = n_y),
             x = rep(1:n_y, times = n_x),
             rand = v_rand,
             sort = v_sort)
tb
#> # A tibble: 64 x 4
#>        y     x  rand  sort
#>    <int> <int> <int> <int>
#>  1     1     1    46    64
#>  2     1     2    15    63
#>  3     1     3    39    62
#>  4     1     4    11    61
#>  5     1     5     8    60
#>  6     1     6    62    59
#>  7     1     7    34    58
#>  8     1     8    14    57
#>  9     2     1     7    56
#> 10     2     2     3    55
#> # … with 54 more rows

Colors

# Re-sort color palette: 
unikn.pal  # from "ds4psy/R/custom_functions.R""
#>   seeblau1 seeblau2 seeblau3 seeblau4   black seegrau4 seegrau3 seegrau2
#> 1  #CCEEF9  #A6E1F4  #59C7EB  #00A9E0 #000000  #666666  #999999  #CCCCCC
#>   seegrau1   white
#> 1  #E5E5E5 #FFFFFF

# (1) Sorted version:
unikn_sort <- unikn.pal[c(4, 4:1, 10:5)]  # 11 colors (seeblau twice) 
# unikn_sort  

# (2) Sorted version with special cases (for small n_x):
if (n_x == 1) {
  unikn_sort <- seeblau                     # 1 color (seeblau)
} else if (n_x == 2) {
  unikn_sort <- unikn.pal[c(4, 2, 5, 9)]    # 4 colors (black, but no white)
} else {
  unikn_sort <- unikn.pal[c(4, 4:1, 10:5)]  # 11 colors (seeblau twice)   
}

unikn_sort    
#>   seeblau4 seeblau4.1 seeblau3 seeblau2 seeblau1   white seegrau1 seegrau2
#> 1  #00A9E0    #00A9E0  #59C7EB  #A6E1F4  #CCEEF9 #FFFFFF  #E5E5E5  #CCCCCC
#>   seegrau3 seegrau4   black
#> 1  #999999  #666666 #000000

Plot themes

ds_theme <- theme_bw() + 
  theme(panel.grid = element_blank(),
        legend.position = "none",
        axis.text = element_blank(),
        axis.ticks = element_blank(),
        panel.background = element_rect(fill = "white"),
        panel.border = element_rect(color = grey(.25, 1)))

ds_theme <- theme_nothing()

Tiles

Tile plots

With thin borders:

# Parameters: 
brd_col <- grey(0, 1)
brd_size <- .10

plot_size <-  3.0  # NORMAL: in cm (used in ggsave below): normal (small) size
# plot_size <- 10.0  # BIG:    in cm (used in ggsave below): when "pix/big_"

pic_path <- "pix/"


# Tile plots: ------ 

# (1a) random version (WITH border lines):
tile_rand <- ggplot(tb) +
  geom_tile(aes(x = x, y = y, fill = rand), col = brd_col, size = brd_size) +
  coord_fixed() + 
  # labs(title = "ds4psy") + 
  labs(x = "Data", y = "Science") +
  # scale_fill_continuous(low = "white", high = seeblau) +
  scale_fill_gradientn(colors = unikn_sort) +
  ds_theme
tile_rand


# Save current plot: 
cur_name <- paste0("tile_rand_", n_x, "_brd.png")
if (plot_size < 10) {
  plot_name <- paste0(pic_path, cur_name)
} else {
  plot_name <- paste0(pic_path, "big_", cur_name)  # insert "big_" prefix
}
ggsave(plot_name, width = plot_size, height = plot_size, units = c("cm"), dpi = 300)

# (1b) sorted version (WITH border lines):
tile_sort <- ggplot(tb) +
  geom_tile(aes(x = x, y = y, fill = sort), col = brd_col, size = brd_size) +
  # coord_polar() +
  coord_fixed() + 
  # labs(title = "ds4psy") + 
  labs(x = "4", y = expression(psi)) +
  # scale_fill_continuous(low = "white", high = seeblau) +
  scale_fill_gradientn(colors = unikn_sort) +
  ds_theme
tile_sort


# Save current plot: 
cur_name <- paste0("tile_sort_", n_x, "_brd.png")
if (plot_size < 10) {
  plot_name <- paste0(pic_path, cur_name)
} else {
  plot_name <- paste0(pic_path, "big_", cur_name)  # insert "big_" prefix
}
ggsave(plot_name, width = plot_size, height = plot_size, units = c("cm"), dpi = 300)

Without thin borders:

# Tile plots: ------ 

# (2a) random version (withOUT border lines):
tile_rand <- ggplot(tb) +
  geom_tile(aes(x = x, y = y, fill = rand))+ #, col = brd_col, size = brd_size) +
  coord_fixed() + 
  # labs(title = "ds4psy") + 
  labs(x = "Data", y = "Science") +
  # scale_fill_continuous(low = "white", high = seeblau) +
  scale_fill_gradientn(colors = unikn_sort) +
  ds_theme
tile_rand


# Save current plot: 
cur_name <- paste0("tile_rand_", n_x, ".png")
if (plot_size < 10) {
  plot_name <- paste0(pic_path, cur_name)
} else {
  plot_name <- paste0(pic_path, "big_", cur_name)  # insert "big_" prefix
}

ggsave(plot_name, width = plot_size, height = plot_size, units = c("cm"), dpi = 300)

# (2b) sorted version (withOUT border lines):

tile_sort <- ggplot(tb) +
  geom_tile(aes(x = x, y = y, fill = sort))+ #, col = brd_col, size = brd_size) +
  # coord_polar() +
  coord_fixed() + 
  # labs(title = "ds4psy") + 
  labs(x = "4", y = expression(psi)) +
  # scale_fill_continuous(low = "white", high = seeblau) +
  scale_fill_gradientn(colors = unikn_sort) +
  ds_theme
tile_sort


# Save current plot: 
cur_name <- paste0("tile_sort_", n_x, ".png")
if (plot_size < 10) {
  plot_name <- paste0(pic_path, cur_name)
} else {
  plot_name <- paste0(pic_path, "big_", cur_name)  # insert "big_" prefix
}

ggsave(plot_name, width = plot_size, height = plot_size, units = c("cm"), dpi = 300)

With thin borders and a numeric label (top left):

# Parameters:
cur_lbl <- paste0(n_x)

x_lbl <- 1 
y_lbl <- (n_y + 1) + n_y/15

# Tile plots: ------ 

# (3a) random version (WITH border lines AND label):
tile_rand_lbl <- ggplot(tb) +
  geom_tile(aes(x = x, y = y, fill = rand), col = brd_col, size = brd_size) +
  geom_text(x = x_lbl, y = y_lbl, label = cur_lbl, size = 2) +
  scale_y_continuous(limits = c(0, y_lbl)) + 
  # geom_text(x = 0, y = n_y, label = cur_lbl, size = 2) +
  # scale_x_continuous(limits = c(0, n_x + 1)) + 
  coord_fixed() + 
  # labs(title = "ds4psy") + 
  labs(x = "Data", y = "Science") +
  # scale_fill_continuous(low = "white", high = seeblau) +
  scale_fill_gradientn(colors = unikn_sort) +
  ds_theme
tile_rand_lbl


# Save current plot: 
cur_name <- paste0("tile_rand_", n_x, "_brd_lbl.png")
if (plot_size < 10) {
  plot_name <- paste0(pic_path, cur_name)
} else {
  plot_name <- paste0(pic_path, "big_", cur_name)  # insert "big_" prefix
}
ggsave(plot_name, width = plot_size, height = plot_size, units = c("cm"), dpi = 300)


# (3b) sorted version (WITH border lines AND label):
tile_sort_lbl <- ggplot(tb) +
  geom_tile(aes(x = x, y = y, fill = sort), col = brd_col, size = brd_size) +
  geom_text(x = x_lbl, y = y_lbl, label = cur_lbl, size = 2) +
  scale_y_continuous(limits = c(0, y_lbl)) + 
  # geom_text(x = 0, y = n_y, label = cur_lbl, size = 2) +
  # scale_x_continuous(limits = c(0, n_x + 1)) + 
  coord_fixed() + 
  # labs(title = "ds4psy") + 
  labs(x = "Data", y = "Science") +
  # scale_fill_continuous(low = "white", high = seeblau) +
  scale_fill_gradientn(colors = unikn_sort) +
  ds_theme
tile_sort_lbl


# Save current plot: 
cur_name <- paste0("tile_sort_", n_x, "_brd_lbl.png")
if (plot_size < 10) {
  plot_name <- paste0(pic_path, cur_name)
} else {
  plot_name <- paste0(pic_path, "big_", cur_name)  # insert "big_" prefix
}
ggsave(plot_name, width = plot_size, height = plot_size, units = c("cm"), dpi = 300)

Polar plots

With thin borders:

# Polar plots: ------ 

pole_rand <- ggplot(tb) +
  geom_tile(aes(x = x, y = y, fill = rand), col = brd_col, size = brd_size) +
  # labs(title = "ds4psy") + 
  coord_polar() + 
  labs(x = "Data", y = "Science") +
  # scale_fill_continuous(low = "white", high = seeblau) +
  scale_fill_gradientn(colors = unikn_sort) +
  ds_theme
pole_rand


# Save current plot: 
plot_name <- paste0("pix/pole_rand_", n_x, "_brd.png")
ggsave(plot_name, width = plot_size, height = plot_size, units = c("cm"), dpi = 300)

pole_sort <- ggplot(tb) +
  geom_tile(aes(x = x, y = y, fill = sort), col = brd_col, size = brd_size) +
  labs(x = "4", y = expression(psi)) +
  coord_polar() +
  # scale_fill_continuous(low = "white", high = seeblau) +
  scale_fill_gradientn(colors = unikn_sort) +
  ds_theme
pole_sort


# Save current plot: 
plot_name <- paste0("pix/pole_sort_", n_x, "_brd.png")
ggsave(plot_name, width = plot_size, height = plot_size, units = c("cm"), dpi = 300)

Without thin borders:

# Polar plots: ------ 

pole_rand <- ggplot(tb) +
  geom_tile(aes(x = x, y = y, fill = rand)) + # , col = brd_col, size = brd_size) +
  # labs(title = "ds4psy") + 
  coord_polar() + 
  labs(x = "Data", y = "Science") +
  # scale_fill_continuous(low = "white", high = seeblau) +
  scale_fill_gradientn(colors = unikn_sort) +
  ds_theme
pole_rand


# Save current plot: 
plot_name <- paste0("pix/pole_rand_", n_x, ".png")
ggsave(plot_name, width = plot_size, height = plot_size, units = c("cm"), dpi = 300)

pole_sort <- ggplot(tb) +
  geom_tile(aes(x = x, y = y, fill = sort)) + # , col = brd_col, size = brd_size) +
  labs(x = "4", y = expression(psi)) +
  coord_polar() +
  # scale_fill_continuous(low = "white", high = seeblau) +
  scale_fill_gradientn(colors = unikn_sort) +
  ds_theme
pole_sort


# Save current plot: 
plot_name <- paste0("pix/pole_sort_", n_x, ".png")
ggsave(plot_name, width = plot_size, height = plot_size, units = c("cm"), dpi = 300)

With thin borders and a numeric label (of the final slice):

# Parameters:
cur_lbl <- paste0(n_x)

if (n_y == 1) {
  y_lbl <- n_y + 0     # no correction
} else if (n_y == 2) {
  y_lbl <- n_y + .75   # small correction
} else if (n_y < 4) {
  y_lbl <- n_y + 1     # constant correction
} else {
 y_lbl <- n_y + n_y/4  # scaled correction (increasing with n_y)
}

# (a) rand version with label:
pole_rand_lbl <- ggplot(tb) +
  geom_tile(aes(x = x, y = y, fill = rand), col = brd_col, size = brd_size) +
  # labs(title = "ds4psy") + 
  # scale_x_continuous(breaks = c(0:n_x), labels = c(0:n_x)) +
  # geom_vline(xintercept = n_x, size = 1, color = "gold") + 
  geom_text(x = n_x, y = y_lbl, label = cur_lbl, size = 2) + 
  coord_polar() + 
  labs(x = "Data", y = "Science") +
  # scale_fill_continuous(low = "white", high = seeblau) +
  scale_fill_gradientn(colors = unikn_sort) +
  ds_theme
pole_rand_lbl


# Save current plot: 
plot_name <- paste0("pix/pole_rand_", n_x, "_brd_lbl.png")
ggsave(plot_name, width = plot_size, height = plot_size, units = c("cm"), dpi = 300)

# (a) sort version with label:
pole_sort_lbl <- ggplot(tb) +
  geom_tile(aes(x = x, y = y, fill = sort), col = brd_col, size = brd_size) +
  # labs(title = "ds4psy") + 
  # scale_x_continuous(breaks = c(0:n_x), labels = c(0:n_x)) +
  # geom_vline(xintercept = n_x, size = 1, color = "gold") + 
  geom_text(x = n_x, y = y_lbl, label = cur_lbl, size = 2) + 
  coord_polar() + 
  labs(x = "Data", y = "Science") +
  # scale_fill_continuous(low = "white", high = seeblau) +
  scale_fill_gradientn(colors = unikn_sort) +
  ds_theme
pole_sort_lbl


# Save current plot: 
plot_name <- paste0("pix/pole_sort_", n_x, "_brd_lbl.png")
ggsave(plot_name, width = plot_size, height = plot_size, units = c("cm"), dpi = 300)

Combine plots

plot_grid(tile_rand, tile_sort)

plot_grid(pole_rand, pole_sort)

plot_grid(tile_rand, pole_sort)

Waves

Data:

N <- 100000

df <- tibble(x = 1:N,
             a = rnorm(n = N, mean =   0, sd = 250),
             # b = rnorm(n = N, mean = 100, sd = 200), 
             c = rnorm(n = N, mean = 500, sd = 200)
)
# df

Density plots:

lwd_all <- 3

ggplot(df) +
  geom_density(aes(x = a), col = seeblau, fill = "black", lwd = lwd_all, alpha = .25) +
  # geom_density(aes(x = b), col = "black", fill = unikn.pal[[5]], alpha = .33) + 
  geom_density(aes(x = c), col = "black", fill = seeblau, lwd = lwd_all, alpha = .33) +
  theme_void()

Histograms (with function curves):

lwd_all <- .75

# overlay histogram and normal density
ggplot(df) +
  geom_histogram(aes(x = c, y = stat(density)), binwidth = 150, col = "white", fill = "black", alpha = .25) +
  geom_histogram(aes(x = a, y = stat(density)), binwidth = 150, col = "white", fill = seeblau, alpha = .25) +
  stat_function(fun = dnorm, args = list(mean = mean(df$c), sd = sd(df$c)), lwd = lwd_all, col = "black", alpha = .95) + 
  stat_function(fun = dnorm, args = list(mean = mean(df$a), sd = sd(df$a)), lwd = lwd_all, col = seeblau, alpha = .95) + 
  scale_x_continuous(limits = c(-2000, 1400)) +
  theme_void() # +

  # annotate(geom = "text", x = -1900, y = .0018, label = course_title, color = "black", alpha = .90, adj = 0, fontface = 2, size = 2.5)

Waves (as curves):

# as curves: 
lwd_all <- .75

ggplot(df) +
  stat_function(fun = dnorm, args = list(mean = 300, sd = 275), lwd = lwd_all, col = "black", alpha = .99) + 
  stat_function(fun = dnorm, args = list(mean =   0, sd = 300), lwd = lwd_all, col = seeblau, alpha = .40) + 
  stat_function(fun = dnorm, args = list(mean = 600, sd = 250), lwd = lwd_all, col = "black", alpha = .40) +
  stat_function(fun = dnorm, args = list(mean = 900, sd = 225), lwd = lwd_all, col = seeblau, alpha = .75) +
  scale_x_continuous(limits = c(-4000, 1700)) +
  theme_void() # +

  # annotate(geom = "text", x = -3900, y = .0016, label = course_title, color = "black", alpha = .90, adj = 0, fontface = 2, size = 2.5)

ggsave("pix/waves.png", width = 15, height = 1.5, units = c("cm"), dpi = 300)

Ideas

ToC bar chart

Idea: Format ToC as bar chart with polar coordinates.

+++ here now +++

# N <- 10  # number of chapters/topics
# nr <- 1:N
# tp <- c("Introduction", "Chapter 2", "Chapter 3", "Chapter 4", "Chapter 5", 
#         "Chapter 6", "Chapter 7", "Chapter 8", "Chapter 9", "Chapter 10")
# val <- 10 + nr

# Table:
toc <- tribble(
  ~nr, ~tp,                 ~ctr,   
  0,  "Introduction",          5,  
  1,    "Basic R",              10,  # was: "Basic R concepts and commands",
  2,    "Visualizing data",      8, 
  3,    "Transforming data",     9, 
  4,    "Exploring data (EDA)", 10, 
  5,    "Tibbles",               6,
  6,    "Importing data",        5, 
  7,    "Tidying data",          9, 
  8,    "Joining data",          7, 
  9,    "Functions",             8,
  10, "Iteration",             7
)

toc <- toc %>% mutate(nr_val = nr + 10) # add constant to increase overall height of bars

# Parameters:
N <- nrow(toc)
max_nr_val <- max(toc$nr_val)
min_ctr <- min(toc$ctr)
max_ctr <- max(toc$ctr)
tol <- 4  # tolerance value (for text labels)
txt_size <- 2.5  # size of txt labels (below)

## Colors: ------ 
# unikn.pal  # basic 

unikn_toc <- c(rev(unikn.pal[5:10]), # white:black (6) 
               unikn.pal[1:4],       # seeblau1 to 4 (4)
               seeblau)              # seeblau4 (1)
# unikn_toc  # 11 colors


## Plots: ------ 

# Chronology:

bar_hori <- ggplot(toc, aes(x = nr)) +
  geom_bar(aes(y = ctr, fill = nr_val), stat = "identity", color = grey(.5, 1)) +
  scale_fill_gradientn(colors = unikn_toc) +
  geom_text(aes(y = ctr + .25, label = tp), angle = 0, adj = 0, size = txt_size) + 
  scale_x_reverse(breaks = 0:N, labels = 0:N) + 
  # scale_x_continuous(breaks = 0:N, labels = 0:N) + 
  scale_y_continuous(limits = c(0, (max_ctr + tol)), breaks = 1:max_ctr, labels = 1:max_ctr) + 
  theme_minimal() + 
  theme(legend.position = "none", panel.grid.minor = element_blank(), panel.grid.major.x = element_blank()) +
  coord_flip() + 
  labs(title = "ds4psy Chronology", x = "Session", y = "Relevance")
bar_hori


# Relevance:

# (1) Bar plots:
bar_vert <- ggplot(toc, aes(x = nr)) +
  geom_bar(aes(y = ctr, fill = ctr), stat = "identity", color = grey(.5, 1)) +
  # scale_fill_gradientn(colors = unikn_toc) +
  scale_fill_gradient(low = "white", high = seeblau, limits = c(min_ctr, max_ctr)) +
  geom_text(aes(y = ctr + .5, label = tp), angle = 90, adj = 0, size = txt_size) + 
  scale_x_continuous(breaks = 0:N, labels = 0:N) + 
  scale_y_continuous(limits = c(0, (max_ctr + tol)), breaks = 1:max_ctr, labels = 1:max_ctr) + 
  theme_minimal() + 
  theme(legend.position = "none", panel.grid.minor = element_blank()) +
  labs(title = "ds4psy Relevance", x = "Session", y = "Relevance")
bar_vert



# (2) Plots on polar coordinates:

topic_clock <- ggplot(toc, aes(x = nr)) +
  geom_bar(aes(y = ctr, fill = nr_val), stat = "identity", color = grey(0, 1), size = .25) +
  scale_fill_gradientn(colors = unikn_toc) +
  # scale_fill_gradient(low = "white", high = seeblau, limits = c(min_ctr, max_ctr)) +
  geom_text(aes(y = max_ctr + tol/2, label = tp), adj = .5, size = txt_size) + 
  scale_x_continuous(breaks = 0:N, labels = 0:N) + 
  scale_y_continuous(limits = c(0, (max_ctr + tol)), breaks = 1:max_ctr, labels = 1:max_ctr) + 
  coord_polar() + 
  # labs(title = "Centrality of topic", x = "Chapter", y = "Topic") + 
  # theme_void() + 
  theme_light() + 
  theme(legend.position = "none", axis.line = element_blank(), 
        axis.title = element_blank(), axis.text.y = element_blank(),
        axis.ticks = element_blank(), panel.border = element_blank())
# topic_clock

centrality <- ggplot(toc, aes(x = nr)) +
  geom_bar(aes(y = ctr, fill = ctr), stat = "identity", color = grey(.10, 1), size = .25) +
  # scale_fill_gradientn(colors = unikn_toc) +
  scale_fill_gradient(low = "white", high = seeblau, limits = c(min_ctr, max_ctr)) +
  geom_text(aes(y = (max_ctr + .65 * tol), label = tp), adj = .5, size = txt_size) + 
  scale_x_continuous(breaks = 0:N, labels = 0:N) + 
  scale_y_continuous(limits = c(0, (max_ctr + tol)), breaks = 1:max_ctr, labels = 1:max_ctr) + 
  coord_polar() + 
  # theme_void() + 
  theme_light() + 
  theme(#legend.position = "none", 
        axis.line = element_blank(), 
        axis.title = element_blank(), axis.text.y = element_blank(),
        axis.ticks = element_blank(), panel.border = element_blank()) +
  labs(fill = "Centrality:") 
centrality



# Highlight current session:
nr_session <- 2

# Add to toc:
toc$session_nr <- 0
toc$session_nr[(nr_session + 1)] <- (max_ctr + tol)  # non-zero value (max)
toc
#> # A tibble: 11 x 5
#>       nr tp                     ctr nr_val session_nr
#>    <dbl> <chr>                <dbl>  <dbl>      <dbl>
#>  1     0 Introduction             5     10          0
#>  2     1 Basic R                 10     11          0
#>  3     2 Visualizing data         8     12         14
#>  4     3 Transforming data        9     13          0
#>  5     4 Exploring data (EDA)    10     14          0
#>  6     5 Tibbles                  6     15          0
#>  7     6 Importing data           5     16          0
#>  8     7 Tidying data             9     17          0
#>  9     8 Joining data             7     18          0
#> 10     9 Functions                8     19          0
#> 11    10 Iteration                7     20          0

cur_session_clock <- ggplot(toc, aes(x = nr)) +
  geom_bar(aes(y = session_nr), stat = "identity", fill = "gold", alpha = .75) +
  geom_bar(aes(y = ctr, fill = ctr), stat = "identity", color = grey(.10, 1), size = .25) +
  # scale_fill_gradientn(colors = unikn_toc) +
  scale_fill_gradient(low = "white", high = seeblau, limits = c(min_ctr, max_ctr)) +
  geom_text(aes(y = (max_ctr + .65 * tol), label = tp), adj = .5, size = txt_size) + 
  scale_x_continuous(breaks = 0:N, labels = 0:N) + 
  scale_y_continuous(limits = c(0, (max_ctr + tol)), breaks = 1:max_ctr, labels = 1:max_ctr) + 
  coord_polar() + 
  # theme_void() + 
  theme_light() + 
  theme(#legend.position = "none", 
        axis.line = element_blank(), 
        axis.title = element_blank(), axis.text.y = element_blank(),
        axis.ticks = element_blank(), panel.border = element_blank()) +
  labs(fill = "Centrality:") 
cur_session_clock

Clock with polar coordinates

[This file last updated on 2019-02-10 16:03:44 by hn.]

Data Science for Psychologists (ds4psy)

Hansjörg Neth, SPDS, uni.kn

2019 02 10

Course Coordinates

Logo

Tiles

Tile plots (random)

Tile plots (sorted)

Tile plots (with borders and numeric labels)

Poles

Pole plots (random)

Pole plots (sorted)

Pole plots (with borders and numeric labels)

Settings

Text

Data

Colors

Plot themes

Tiles

Tile plots

Polar plots

Combine plots

Waves

Ideas

ToC bar chart